Is there any use of using 3D convolutions for traditional images (like cifar10, imagenet)?
As far as I've seen there is no use of 3D CNNs for traditional image classification tasks.

The reason I think is that, while these images do have multiple channels, there is no spatial information in those channels for the 3D convolution to extract. On the other hand it makes more sense to take the weighted sum of those pixels along that dimension (as the 2D convolution does).

3D CNNs have been used, as far as I know, only for applications where you have volumetric data, i.e. the images are sequential and, when combined, form a large 3D image.